Word2Vec is a group of neural network models that learn word embeddings by predicting neighboring words (Skip-gram) or context words (CBOW). It fundamentally changed how NLP handles text by capturing semantic relationships in continuous vector space. Despite its age, Word2Vec remains influential in embedding research.